• Data science engineer
• Deep learning engineer
• Data science developer
• Deep learning research engineer
• Data science associate
• Deep learning developer
• Associate data analyst
• Data analyst
• Data analyst (specialist)
• Data architect
• Python analyst
• Python developer
• Research analyst
• Business analyst
• Machine learning engineer
• Machine learning developer
• Machine learning expert
• Machine learning specialist
• Data Science with R - Machine learning techniques, analytics, and data manipulation
• Data Science with Python - Packages, scripts, modules, nested loops, and strings
• Introduction to Data Science
• Introduction to Statistics
• Data Visualization
• Exploratory Data Analysis
• Regression Analysis
• Subprocess Module
• Random Module
• Regular Expressions
• Built-in Functions
• Command-Line Arguments
• Extended Iterable Unpacking
• Python Implementations
• Neural Networks
• Regression Analysis - Linear regression and non-linear regression
• Pattern Recognition - Clustering
• Data Exploration
#1 Identifying job-fit candidates based on job roles
You can build customized data science skill assessments for any given job role. Using this capability, you can choose questions from different skill types, including functional, technical, and soft skills. For example, with our customized data scientist coding assessment, you can assess candidates' understanding of Data Extraction and Mining, Numerical Ability, Interactive Reporting, Python Coding, and Machine Learning and hire the best individuals for the job.
#2 Skill-gap analysis of your employees
iMocha allows you to trace employees' skill competency through data science training assessments. It determines the current skill level and identifies the areas for growth. Using this feature, you can measure employees' progress from their existing knowledge base to gained knowledge. For example, you can use our data science training assessments to identify an engineer's knowledge about Sampling Distribution, SQLite Coding, Quantitative Aptitude, and other skills and perform a skill gap analysis.
We provide various types of data scientist hiring tests to help you evaluate candidates' specific skills. These questions are created by Subject Matter Experts (SMEs) based on their knowledge and expertise. For example, only Data Science specialists will develop questions about Data Manipulation using R or Machine Learning based on easy, medium, and hard difficulty levels.
You can - choose which questions to include in the Data Scientist coding test or ask us to create personalized assessments according to your requirements.
Our SMEs can create individualized assessments depending on your job role requirements. These assessments are divided into primary and secondary skills, such as Data Extraction, Data Visualization, Regression Analysis, Quantitative Aptitude, Optimizing Functions, Reporting, and more. Additionally, SMEs can craft customized questions according to applicants' experience and difficulty level.
Some popular Certifications for Data science related job roles are as follows:
• Microsoft Certified: Azure Data Scientist Associate
• IBM Data Science Professional Certificate
• Google Professional Data Engineer Certification
• Cloudera Certified Professional (CCP) Data Engineer
• SAS Certified AI & Machine Learning Professional
• TensorFlow Developer Certificate
• Open Certified Data Scientist (Open CDS)
• Principal Data Scientist (PDS)
• Certified Analytics Professional (CAP)
• HarvardX Data Science Professional Certificate
Some of the common data science interview questions asked for this role are:
• Why is normal distribution important?
• Can you use machine learning for time series analysis?
• What is a lambda function in Python?
• What is a hyperplane in SVM?
• How is memory managed in Python?
Want more data science interview questions? Here is a list of 100+ data science interview questions you can ask data science professionals.
Data science engineers are required to perform the following tasks and responsibilities:
• Should be able to collect data and identify its data source
• Analyze both structured and unstructured data
• Create workflows to solve the business problems
• Collaborate with the team to develop data strategies
• Know how to combine various algorithms
• Should have the ability to utilize data visualization techniques and tools
• Present AI/ML solutions for business processes and outcomes
• Monitor data pipelines and conduct knowledge-sharing sessions for effective data use
• Conduct data mining and extraction for valuable data sources
• Analyze enterprise databases to simplify and improve product development
• Develop the organization’s test model quality and A/B testing framework
You can consider these technical as well as non-technical skills while hiring Data science engineers:
• Data Visualization
• Statistical Analysis
• Machine Learning and AI
• Deep Learning
• Data Wrangling
• Big Data
• Processing Large Data
• Web Scraping
• Database Management
• Data Mining
• Data Intuition
• Problem-solving skills
• Interpersonal skills
• Communication skills
• Detail Oriented
• Logical Reasoning
In the United States, the average Data science engineer salary is $124,244 per year. Entry-level Data science engineers' salaries start at $97,462 per year.